Data Conversion Research Articles

Secrets of Event-Based Optical Flow, Depth and Ego-Motion Estimation by Contrast Maximization.

Event cameras respond to scene dynamics and provide signals naturally suitable for motion estimation with advantages, such as high dynamic range. The emerging field of event-based vision motivates a revisit of fundamental computer vision tasks related to motion, such as optical flow and depth estimation. However, state-of-the-art event-based optical flow methods tend to originate in frame-based deep-learning methods, which require several adaptations (data conversion, loss function, etc.) as they have very different properties. We develop a principled method to extend the Contrast Maximization framework to estimate dense optical flow, depth, and ego-motion from events alone. The proposed method sensibly models the space-time properties of event data and tackles the event alignment problem. It designs the objective function to prevent overfitting, deals better with occlusions, and improves convergence using a multi-scale approach. With these key elements, our method ranks first among unsupervised methods on the MVSEC benchmark and is competitive on the DSEC benchmark. Moreover, it allows us to simultaneously estimate dense depth and ego-motion, exposes the limitations of current flow benchmarks, and produces remarkable results when it is transferred to unsupervised learning settings. Along with various downstream applications shown, we hope the proposed method becomes a cornerstone on event-based motion-related tasks.

Research on integration technology of architectural CAD and budget estimate

It is particularly important to integrate architectural CAD and budget integration technology in the field of modern construction project management. Faced with the increasing complexity of construction projects, the method of separating design and budget in the past is no longer suitable for the standards of efficient management. Starting from the basic concepts and core technologies of architectural CAD, this article studies the difficulties in data transmission and conversion, as well as the strong demand for integration technology throughout the entire building lifecycle. Analyzing the implementation methods of integrating architectural CAD and budget technology, including data docking, the application of BIM technology, and collaborative work modes, this article explains the positive role of this integration in enhancing the unity of design and budget, improving change response speed, and reducing costs, providing reference for the information development of the construction industry.

Cartographic animations, a supporting technology for education

Abstract. To date, basic theoretical and methodological ideas of animated cartography have been formed, as well as general schemes for animated maps compilation. However, the technological issue remains relevant in this domain due to the abundance of modern tools that can be used for compilation of animated maps. Particularly, most of currently implemented cartographic animation methodologies require the use of a variety of specialized software, including geographic information systems (GISs), vector and raster graphical editors, video effects editing software, and others. So the data processing and conversion techniques are extremely complicated in some cased, when data converted from one software platform to another.In our study, we established investigation of popular desktop GISs functionality application as a full-cycle animated cartography environment. In such a context, we discover approaches to improve methodology of animated maps automated compilation. To prototype a full-cycle animation in GIS environment, we developed a set of animation algorithms implemented in QGIS and documented the algorithms using UML (Unified Modeling Language) diagrams.

Monitoring batch suspension polymerization process based on calorimetric-state estimation technique

This work introduces an approach to enhance the real-time monitoring of batch suspension polymerization processes through the integration of real-time calorimetry and state estimation techniques. The challenges inherent in monitoring dynamic and complex processes, particularly in the absence of direct measurements, are effectively addressed through the proposed approach. A dynamic model equation specific to batch operation is employed to assess conversion throughout the process, with timely updates of dynamic parameters in the model equation. A nonlinear high gain cascaded observer with calorimetric measurements estimates the overall heat transfer coefficient and reaction heat. Variations in other parameters such as heat capacity and density are derived from estimated conversion data and updated in the model equation. The study also accounts for the heat loss to the surroundings during isoperibolic batch processes. Validation of the proposed soft sensor model is carried out by comparing the estimated and experimental conversion data by conducting MMA suspension polymerization in a reaction calorimeter. Results demonstrate the potential of calorimetric state estimation as an alternative to direct conversion measurements, offering comprehensive and enhanced monitoring of the batch suspension polymerization process.

Modelling greenhouse gas emissions of land use and land-use change using spatially explicit land conversion data for French crops

Modelling greenhouse gas emissions of land use and land-use change using spatially explicit land conversion data for French crops

Performance Comparison between Deep Neural Network and Machine Learning based Classifiers for Huntington Disease Prediction from Human DNA Sequence.

Huntington Disease (HD) is a type of neurodegenerative disorder which causes problems like psychiatric disturbances, movement problem, weight loss and problem in sleep. It needs to be addressed in earlier stage of human life. Nowadays Deep Learning (DL) based system could help physicians provide second opinion in treating patient's disease. In this work, human Deoxyribo Nucleic Acid (DNA) sequence is analyzed using Deep Neural Network (DNN) algorithm to predict the HD disease. The main objective of this work is to identify whether the human DNA is affected by HD or not. Human DNA sequences are collected from National Center for Biotechnology Information (NCBI) and synthetic human DNA data are also constructed for process. Then numerical conversion of human DNA sequence data is done by Chaos Game Representation (CGR) method. After that, numerical values of DNA data are used for feature extraction. Mean, median, standard deviation, entropy, contrast, correlation, energy and homogeneity are extracted. Additionally, the following features such as counts of adenine, thymine, guanine and cytosine are extracted from the DNA sequence data itself. The extracted features are used as input to the DNN classifier and other machine learning based classifiers such as NN (Neural Network), Support Vector Machine (SVM), Random Forest (RF) and Classification Tree with Forward Pruning (CTWFP). Six performance measures are used such as Accuracy, Sensitivity, Specificity, Precision, F1 score and Mathew Correlation Co-efficient (MCC). The study concludes DNN, NN, SVM, RF achieve 100% accuracy and CTWFP achieves accuracy of 87%.

Controller Area Network (CAN) Bus Transceiver with Authentication Support and Enhanced Rail Converters

This paper presents an advanced Controller Area Network (CAN) bus transceiver designed to enhance security using frame-level authentication with the concept of a nonphysical virtual auxiliary data channel. We describe the newly conceived transceiver security features and provide results concerning the design, implementation, fabrication and test of the transceiver to validate its functionality and robust operation in the presence of systemic error sources including Process, Voltage, and Temperature (PVT) variations. The virtual auxiliary channel integrates CAN frame authentication signatures into the primary data payload via phase modulation while also providing compatibility with existing CAN protocols, interoperability with non-enhanced systems and requiring no network or software modifications. Enhanced rail converters are designed to facilitate single-rail to dual-rail data conversion and vice versa, preserving phase information and minimizing phase errors across various nonideal effects such as frequency drift, Process, Voltage, and Temperature (PVT) variations, and cable phase mismatch. This ensures reliable data transmission and robust authentication in the presence of adversarial cyberattacks such as packet injection. The receiver recovers both the CAN frame data and the security signature, comparing the latter with an authorized signature to provide a real-time “GO/NO_GO” signal for verifying packet authenticity and without exceeding the CAN clock jitter specifications.

Fluid Grey 2: How Well Does Generative Adversarial Networks Learn Deeper Topology Structure in Architecture That Matches Images?

Taking into account the regional characteristics of ‘intrinsic’ and ‘extrinsic’ properties of space is an essential issue in architectural design and urban renewal, which is often achieved step by step using image and graph-based GANs. However, each model nesting and data conversion may cause information loss, and it is necessary to streamline the tools to facilitate architects and users to participate in the design. Therefore, this study hopes to prove that I2I GAN also has the potential to recognize topological relationships autonomously. Therefore, this research proposes a method for quickly detecting the ability of pix2pix to learn topological relationships, which is achieved by adding two Grasshopper-based detection modules before and after GAN. At the same time, quantitative data is provided and its learning process is visualized, and changes in different input modes such as greyscale and RGB affect its learning efficiency. There are two innovations in this paper: 1) It proves that pix2pix can automatically learn spatial topological relationships and apply them to architectural design. 2) It fills the gap in detecting the performance of Image-based Generation GAN from a topological perspective. Moreover, the detection method proposed in this study takes a short time and is simple to operate. The two detection modules can be widely used for customizing image datasets with the same topological structure and for batch detection of topological relationships of images. In the future, this paper may provide a theoretical foundation and data support for the application of architectural design and urban renewal that use GAN to preserve spatial topological characteristics.

A method for data conversion of exposure level testers using sinusoidal alternating magnetic fields

Flux density measurements provide valuable information on the state of electrical appliances, which can be utilised for improving the safety and the performance of the associated system. Exposure level testers with a B-field probe are widely used for flux density measurements but they can only perform a time-domain measurement and display it as output voltage related to flux density. In other words, they cannot output flux density directly, which brings many inconveniences. These drawbacks can be improved using data conversion coefficients. However, the test frequencies of most exposure level testers are greater than 1 Hz. They are unable to calculate the data conversion coefficients using a constant magnetic field. This paper proposes a method for data conversion of exposure level testers using alternating magnetic fields to measure flux density directly: the flux density conversion model was established; the mathematical relationship between the mean value of the voltage signal output by the exposure level testers and its stable display value was deduced; and an experimental test of the conversion coefficients was carried out. The results show that the error between the value measured using the new technique and the display value of the exposure level tester at different frequencies is small. This method can trace the flux density in both the time and frequency domains through software processing algorithms without changing the exposure level tester itself. By applying sinusoidal magnetic fields and using the conversion factor, then existing testers can be used to measure flux density directly.

Digital detection of Alzheimer’s disease using smiles and conversations with a chatbot

In super-aged societies, dementia has become a critical issue, underscoring the urgent need for tools to assess cognitive status effectively in various sectors, including financial and business settings. Facial and speech features have been tried as cost-effective biomarkers of dementia including Alzheimer’s disease (AD). We aimed to establish an easy, automatic, and extensive screening tool for AD using a chatbot and artificial intelligence. Smile images and visual and auditory data of natural conversations with a chatbot from 99 healthy controls (HCs) and 93 individuals with AD or mild cognitive impairment due to AD (PwA) were analyzed using machine learning. A subset of 8 facial and 21 sound features successfully distinguished PwA from HCs, with a high area under the receiver operating characteristic curve of 0.94 ± 0.05. Another subset of 8 facial and 20 sound features predicted the cognitive test scores, with a mean absolute error as low as 5.78 ± 0.08. These results were superior to those obtained from face or auditory data alone or from conventional image depiction tasks. Thus, by combining spontaneous sound and facial data obtained through conversations with a chatbot, the proposed model can be put to practical use in real-life scenarios.

Not in the file: How competency committees work with undocumented contributions.

Competence committees (CCs) centre their work around documentation of trainees' performance; undocumented contributions (i.e. informal, unrecorded material like personal judgements, experiential anecdotes and contextual information) evoke suspicion even though they may play a role in decision making. This qualitative multiple case study incorporates insights from a social practice perspective on writing to examine the use of undocumented contributions by the CCs of two large post-graduate training programmes, one in a more procedural (MP) speciality and the other in a less procedural (LP) one. Data were collected via observations of meetings and semi-structured interviews with CC members. In the analysis, conversations were organised into triptychs of lead-up, undocumented contribution(s), and follow-up. We then created thick descriptions around the undocumented contributions, drawing on conversational context and interview data to assign possible motivations and significance. We found no instances in which undocumented contributions superseded the contents of a trainee's file or stood in for missing documentation. The number of undocumented contributions varied between the MP CC (six instances over two meetings) and the LP CC (22 instances over three meetings). MP CC discussions emphasised Entrustable Professional Activity (EPA) observations, whereas LP CC members paid more attention to narrative data. The divergent orientations of the CCs-adding an 'advis[ing]/guid[ing]' role versus focusing simply on evaluation-offers the most compelling explanation. In lead-ups, undocumented contributions were prompted by missing and flawed documentation, conflicting evidence and documentation at odds with members' perceptions. Recognising other 'red flags' in documentation often required professional experience. In follow-ups, purposes served by undocumented contributions varied with context and were difficult to generalise; we, therefore, provide deeper analysis of two vignettes to illustrate. Our data suggest undocumented contributions often serve best efforts to ground decisions in documentation. We would encourage CC practices and policies be rooted in more nuanced approaches to documentation.

Imprints of vector field non-standard couplings on solar neutrino flux

Regarding the proximity of dark energy scale and neutrino mass, one can get new insight into the probe signatures of vector field dark energy component on neutrino flavor conversion. A vector field is assumed to be conformally coupled to various matter components through a screening paradigm. We show how the oscillation probabilities evolve differently with a finite-size matter density like the Sun for different values of relevant model parameters. Considering existing neutrino flavor conversion data, we point out how this non-standard coupling affects neutrino fluxes, and also, we compute the discrepancy in the total flux of solar electron-neutrinos.

Transform Financial Data into Strategic Insights Using the Sap Business Technology Platform

This article examines the groundbreaking fusion of SAP Business Technology Platform (BTP) and Artificial Intelligence (AI) in the realm of financial reporting, emphasizing their transformative role in translating financial data into strategic insights for executive leadership. By introducing automated commentary generation, this integration enables BTP to convert intricate financial datasets into coherent narratives that furnish decision-makers with actionable intelligence. Positioned at the nexus of AI progress and financial analysis, BTP facilitates the seamless conversion of complex data into narrative or commentary reports, fostering coherence, insightfulness, and accessibility for decision-makers. The discourse delves into the underlying mechanisms of BTP functionality in financial reporting, spanning data interpretation, language processing, commentary generation, and customization, thereby enhancing report efficiency, precision, and accessibility. Through meticulous exploration, this article elucidates BTP's contributions to financial reporting commentary, including enhanced efficiency, accuracy, scalability, and personalized delivery of financial insights. Furthermore, it confronts the challenges inherent in BTP application, encompassing technical intricacies, ethical considerations, and the constraints of current technologies in capturing financial intricacies. Looking ahead, the article envisions a future where ongoing advancements in AI and machine learning refine BTP capabilities, furnishing even more sophisticated and nuanced financial insights to underpin strategic decision-making at the highest echelons of business leadership.

Real-Time and Long-Term Monitoring of Coastal Water Turbidity Using an Ocean Buoy Equipped with an ADCP.

In this study, an acoustic Doppler current profiler (ADCP) operating at 600 kHz was installed on an ocean data buoy in the Qigu waters, Taiwan, to gather real-time sound echo intensity data. These data were then correlated with turbidity measurements obtained by a turbidimeter mounted on the buoy's mooring line at a water depth of 13 m. The data buoy operated from 6 June to 16 August 2017. During this period, turbidity measurements were recorded from 6 to 21 June 2017. This study established a calibration between the sound echo intensity measured by the ADCP and the turbidity measured using the turbidimeter; a strong linear correlation was discovered between these two variables. This correlation enabled the conversion of echo intensity data into a continuous time series of turbidity measurements, facilitating real-time and long-term monitoring of coastal water turbidity through the deployment of a buoy equipped with an ADCP. The relationships between turbidity and environmental factors such as rainfall, tides, current speeds, and wave activity over an extended period were then investigated. The results revealed that stronger tides and currents in the Qigu waters often lead to higher turbidity, suggesting that these two factors are the primary driving forces for sediment transport in the Qigu waters. Additionally, sampling of water in the Qigu area revealed sediment particles of size ranging from 2 to 120 μm.

Two-Step Clustering for Mineral Prospectivity Mapping: A Case Study from the Northeastern Edge of the Jiaolai Basin, China

The advancement of geological big data has rendered data-driven methodologies increasingly vital in Mineral Prospectivity Mapping. The effective integration of quantitative and qualitative data, including experiential and knowledge-based insights, is crucial in geological data fusion. Specifically, the conversion of raw data into samples and the selection of predictive methods are two core issues that constitute the focus of this study. Traditional clustering methods require the user to specify the number of clusters in advance. The two-step clustering can automatically determine the clustering result ‘k’ while analyzing both continuous and categorical variables, by building a Cluster Feature (CF) and using information criteria to merge nodes. In this study, we conducted an analysis utilizing stream sediment element data, residual gravity anomalies, and fault distribution through the two-step clustering method. Factor analysis (FA) was employed to reduce 16 elemental variables from stream sediments into five uncorrelated continuous variables; additionally, residual gravity anomalies were transformed from continuous to categorical variables via an interval-based method before being combined with fault distribution, resulting in seven variables for clustering. The research findings indicate that categorical variables significantly influence clustering results; concurrently, as the importance of continuous variables within the cluster increases, so does k. When only one categorical variable is present, residual gravity anomalies show significantly better clustering than fault distribution; however, when two categorical variables are involved, it is essential to consider the quantity of categories: more categories lead to poorer quality. The results from the Jiaolai Basin’s northeastern margin indicate a significant correlation with known gold deposits; two-step clustering is a promising and effective method for improving mineral prospecting efforts.

Capturing multiple emotions from conversational data using fine tuned transformers

Emotion detection is one of the crucial topics of Natural Language Processing (NLP) in recent years, and now one of the biggest motivating factors in correct identification and interpretation of a wide range of emotional expressions in textual data. This research examines the use of Bidirectional Encoder Representations from Transformers (BERT), a trained transformer model for emotion detection in textual data. This analysis evaluates how good BERT is at identifying emotions such as surprise, anger, fear, happiness and sadness compared with ordinary machine learning as well as deep learning techniques. The addition of weighted emotions approach enhances the model performance and gives a deeper emotional context awareness making it more effective to deal with complicated emotional utterances. The purpose of the research is to develop a fine-tuned BERT model with a weighted emotion framework to enhance the accuracy of emotion classification in conversational text. The context is improving emotion recognition in scenarios where multiple emotions co-exist, addressing limitations in traditional models by capturing subtle and overlapping emotional expressions. In terms of training the methods multiple datasets are considered and also the research examines various models performance. The article further discusses possible application areas of BERT modified in light of NLP.

Utilizing 3D seismic velocities within the Triassic Bacton Group to estimate the exhumation of the Rotliegend Group, Blocks 48/8 and 48/9, UK Southern North Sea

In the UK Southern North Sea, the exploration of pre-Zechstein structures depends upon accurate depth conversion of reflection seismic data. In Quadrant 48, depth conversion by conventional methods, such as linear velocity v. depth methods ( V = kZ + V 0 , or ‘ V 0 K ’), is challenging due to the influence of the Sole Pit Inversion. This leads to velocities being higher over the inversion axis than present-day burial would suggest and strong lateral velocity gradients within consistent intervals. To overcome this, velocity modelling was undertaken utilizing advanced seismic velocity extraction techniques to improve spatial resolution over that of well-based point data alone. The seismic velocities have been used to calculate the post-burial uplift of the Bacton Group, using seismic velocities of the unit. The results, which have been calibrated for local halokinesis, approximate Rotliegend Group uplift and aim to further de-risk the depth conversion uncertainty at target level, offering new insight into the inversion of the pre-salt section, with implications for future hydrocarbon exploration.

BIM and IFC Data Readiness for AI Integration in the Construction Industry: A Review Approach

Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration.

A Generic Transformation Approach for Complex Laboratory Data Using the Fast Healthcare Interoperability Resources Mapping Language: Method Development and Implementation.

Reaching meaningful interoperability between proprietary health care systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from the source to target systems. The Mirth Connect Server, an open-source communication server, offers, in addition to the exchange functionality, functions for simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) has recently become increasingly prevalent in national health care systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR mapping language (FML). In this study, a generic approach is developed, which allows for the application of declarative mapping rules defined using FML in an exchangeable manner. A transformation engine is required to execute the mapping rules. FHIR natively defines resources to support the conversion of instance data, such as an FHIR StructureMap. This resource encodes all information required to transform data from a source system to a target system. In our approach, this information is defined in an implementation-independent manner using FML. Once the mapping has been defined, executable Mirth channels are automatically generated from the resources containing the mapping in JavaScript format. These channels can then be deployed to the Mirth Connect Server. The resulting tool is called FML2Mirth, a Java-based transformer that derives Mirth channels from detailed declarative mapping rules based on the underlying StructureMaps. Implementation of the translate functionality is provided by the integration of a terminology server, and to achieve conformity with existing profiles, validation via the FHIR validator is built in. The system was evaluated for its practical use by transforming Labordatenträger version 2 (LDTv.2) laboratory results into Medical Information Object (Medizinisches Informationsobjekt) laboratory reports in accordance with the National Association of Statutory Health Insurance Physicians' specifications and into the HL7 (Health Level Seven) Europe Laboratory Report. The system could generate complex structures, but LDTv.2 lacks some information to fully comply with the specification. The tool for the auto-generation of Mirth channels was successfully presented. Our tests reveal the feasibility of using the complex structures of the mapping language in combination with a terminology server to transform instance data. Although the Mirth Server and the FHIR are well established in medical informatics, the combination offers space for more research, especially with regard to FML. Simultaneously, it can be stated that the mapping language still has implementation-related shortcomings that can be compensated by Mirth Connect as a base technology.

Meta-analysis accelerator: a comprehensive tool for statistical data conversion in systematic reviews with meta-analysis

BackgroundSystematic review with meta-analysis integrates findings from multiple studies, offering robust conclusions on treatment effects and guiding evidence-based medicine. However, the process is often hampered by challenges such as inconsistent data reporting, complex calculations, and time constraints. Researchers must convert various statistical measures into a common format, which can be error-prone and labor-intensive without the right tools.ImplementationMeta-Analysis Accelerator was developed to address these challenges. The tool offers 21 different statistical conversions, including median & interquartile range (IQR) to mean & standard deviation (SD), standard error of the mean (SEM) to SD, and confidence interval (CI) to SD for one and two groups, among others. It is designed with an intuitive interface, ensuring that users can navigate the tool easily and perform conversions accurately and efficiently. The website structure includes a home page, conversion page, request a conversion feature, about page, articles page, and privacy policy page. This comprehensive design supports the tool’s primary goal of simplifying the meta-analysis process.ResultsSince its initial release in October 2023 as Meta Converter and subsequent renaming to Meta-Analysis Accelerator, the tool has gained widespread use globally. From March 2024 to May 2024, it received 12,236 visits from countries such as Egypt, France, Indonesia, and the USA, indicating its international appeal and utility. Approximately 46% of the visits were direct, reflecting its popularity and trust among users.ConclusionsMeta-Analysis Accelerator significantly enhances the efficiency and accuracy of meta-analysis of systematic reviews by providing a reliable platform for statistical data conversion. Its comprehensive variety of conversions, user-friendly interface, and continuous improvements make it an indispensable resource for researchers. The tool’s ability to streamline data transformation ensures that researchers can focus more on data interpretation and less on manual calculations, thus advancing the quality and ease of conducting systematic reviews and meta-analyses.